You want to optimize a large BigQuery table for queries filtering on country and event_date. Which dbt config best applies warehouse-specific optimizations?

hard🚀 Application Q8 of 15

dbt - Performance Optimization

You want to optimize a large BigQuery table for queries filtering on country and event_date. Which dbt config best applies warehouse-specific optimizations?

Aconfig(materialized='table', partition_by={'field': 'country', 'data_type': 'string'})

Bconfig(materialized='incremental', cluster_by=['event_date', 'country'])

Cconfig(materialized='table', partition_by={'field': 'event_date', 'data_type': 'date'}, cluster_by=['country'])

Dconfig(materialized='view', cluster_by=['country', 'event_date'])

Step-by-Step Solution

Solution:

Step 1: Choose partitioning column
Partitioning by event_date (a date) is ideal for time-based filtering in BigQuery.
Step 2: Choose clustering column
Clustering by country groups data to speed up queries filtering by country.
Step 3: Verify materialization
Materializing as a table supports partitioning and clustering.
Final Answer:
config(materialized='table', partition_by={'field': 'event_date', 'data_type': 'date'}, cluster_by=['country']) -> Option C
Quick Check:
Partition by date + cluster by country for BigQuery [OK]

Quick Trick: Partition by date, cluster by country for BigQuery tables [OK]

Common Mistakes:

MISTAKES

Partitioning by string column instead of date
Using incremental materialization without partitioning
Trying to cluster views which is unsupported

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You want to optimize a large BigQuery table for queries filtering on country and event_date. Which dbt config best applies warehouse-specific optimizations?

Step 1: Choose partitioning column

Step 2: Choose clustering column

Step 3: Verify materialization

Final Answer:

Quick Check:

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